---
title: "Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(p8105.datasets)

library(plotly)
```

Column {data-width=650}
-----------------------------------------------------------------------

### Distribution of departments by aisles for less than 4 days since prior order

```{r}
data("instacart") 

instacart_3 = instacart %>% 
  
 filter(days_since_prior_order < 4,
        department %in% c("bakery", "snacks", "canned goods")) %>% 
 arrange(aisle, department) %>%
  
  
plot_ly(
    x = ~department, y = ~aisle, type = "scatter", mode = "markers",
    color = ~days_since_prior_order,  alpha = 0.5)
```

Column {data-width=350}
-----------------------------------------------------------------------

### Number of items ordered in each aisle >20000

```{r}
data("instacart") 

instacart_1 = instacart %>% 
count(aisle) %>% 
  arrange(desc(n)) %>% 
  filter(n > 20000) %>% 
  mutate(aisle = fct_reorder(aisle, n)) %>% 
  plot_ly(x = ~aisle, y = ~n, color = ~aisle, type = "bar", colors = "viridis") 
```

### The mean hour of the day at which top 3 most frequently ordered products are ordered

```{r}
data("instacart") 

instacart_2 = instacart %>% 

filter(product_name %in% c("Banana", "Bag of Organic Bananas", "Organic Strawberries")) %>%
  group_by(product_name, order_dow) %>%
  summarize(mean_hour = mean(order_hour_of_day)) %>% 
  arrange(product_name, order_dow, mean_hour) %>% 
  mutate(product_name = fct_reorder(product_name, mean_hour)) %>%   
  plot_ly(y = ~mean_hour, color = ~product_name, type = "box", colors = "viridis")

```